diff --git a/tensorrt_llm/_torch/auto_deploy/models/custom/modeling_qwen3_5_moe.py b/tensorrt_llm/_torch/auto_deploy/models/custom/modeling_qwen3_5_moe.py
index b44c5f040982..b68f7e718bd2 100644
--- a/tensorrt_llm/_torch/auto_deploy/models/custom/modeling_qwen3_5_moe.py
+++ b/tensorrt_llm/_torch/auto_deploy/models/custom/modeling_qwen3_5_moe.py
@@ -780,13 +780,14 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             layer_type="moe",
         )
 
-        # Single merge-point all_reduce for routed + shared partial sums.
-        # Both branches produce per-rank partial outputs under TP/EP sharding
-        # (routed: MoEShardableNode; shared: rowwise down_proj inside the MLP).
-        # One reduction on the sum lifts both to full; reducing before the add
-        # would mix a full routed contribution with an unreduced shared one.
-        expert_output = expert_output + shared_expert_output
+        # The shared expert is replicated (Qwen3_5MoeMLP intentionally omits
+        # ``layer_type`` and the yaml ``shard_layers`` whitelist excludes it),
+        # so its output is already the full value on every rank. All-reduce
+        # the sharded routed-expert partial first, then add the replicated
+        # shared output; adding before would scale the shared output by the
+        # TP world size.
         expert_output = torch.ops.auto_deploy.all_reduce(expert_output, layer_type="moe")
+        expert_output = expert_output + shared_expert_output
 
         expert_output = expert_output.reshape(batch_size, sequence_length, hidden_dim)
         return expert_output